Technical Field
Embodiments of the present disclosure are directed to the monitoring of industrial assets based on physical sensor data to detect precursor events of major system failures.
Discussion of the Related Art
Anomaly detection from sensor data is an important application of data mining. Using off-shore oil production as an example, to ensure safety of workers and prevent major system failures, remote monitoring of equipment is a critical part of offshore oil production. A key task of remote monitoring is anomaly detection, i.e., to detect early indications of failures before their occurrence. For example, compressors are a key component to ensure stable production, and for this reason are monitored.
Much effort has been devoted to automate anomaly detection, but it is still a very challenging task. There are several technical challenges. Sensor data, such as temperature, pressure, displacement, flow rate, vibration, etc., is noisy, sensor values may change discontinuously, and the correlational structure can change even on a daily basis. There is a need to incorporate an intelligent method to automatically remove unwanted noise. Variable dependency matters, in that variables should not be analyzed separately, as this may generate false alerts. In addition, the system being monitored is frequently unstable, as operating conditions change over time. There is also a need for diagnostic information such as which variables exhibit anomalies. However, prior art methods, such as k-means and principle component analysis (PCA), are known to have serious issues in practice, and cannot both handling multiple operating modes and multivariate variable-wise anomaly scoring. Most notably, PCA cannot effectively provide variable-wise information. This is problematic in most industrial applications, where dimensionality is often greater than 100.